Thakur College of Engineering and Technology
Kandivali (East)
Mumbai-101

abstract

Data mining is the process of extracting patterns from data. Data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.
Data mining can be used to uncover patterns in data but is often carried out only on samples of data. The mining process will be ineffective if the samples are not a good representation of the larger body of data. Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being "mined". Inability to find patterns may become a cause for some disputes between customers and service providers. Therefore data mining is not fool proof but may be useful if sufficiently representative data samples are collected. The discovery of a particular pattern in a particular set of data does not necessarily mean that a pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the process is the verification and validation of patterns on other samples of data.
The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques to sample sizes that are (or may be) too small for statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). Data dredging may, however, be used to develop new hypotheses, which must then be validated with sufficiently large sample sets.

Prepared by:
GOWDHAMI.D
M.VEERAMANIMEKALADATA MINING.pptx (Size: 854.75 KB / Downloads: 177)
DATA MINING-DEFINITION
Data mining is the process of processing large volumes of data (usually stored in a database), searching for patterns and relationships within that data. Retail outlets using data mining might discover that many customers who buy beer also buy diapers. They may then increase sales by positioning the two together.